Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting

Embedded motivational interviewing combined with a smartphone app to increase physical activity in people with sub-acute low back pain: study protocol of a cluster randomised control trial

Background: Motivational Interviewing is an evidence-based, client-centred counselling technique that has been used effectively to increase physical activity, including for people with low back pain. One barrier to implementing Motivational Interviewing in health care settings more broadly is the extra treatment time with therapists. The aim of this paper is to describe the design of a cluster randomised controlled trial evaluating the effect of an intervention that pairs Motivational Interviewing embedded into usual physiotherapy care with a specifically designed app to increase physical activity in people with sub-acute low back pain. Methods: The study is a cluster randomised controlled in which patients aged over 18 years who have sub-acute low back pain (3–12 weeks duration) are recruited from four public hospital outpatient clinics. Based on the recruitment site, participants either receive usual physiotherapy care or the Motivational Interviewing intervention over 6 consecutive weekly outpatient sessions with a specifically designed app designed to facilitate participant-led physical activity behaviour change in between sessions. Outcome measures assessed at baseline and 7 weeks are: physical activity as measured by accelerometer (primary outcome), and pain-related activity restriction and pain self-efficacy (secondary outcomes). Postintervention interviews with physiotherapists and participants will be conducted as part of a process evaluation. Discussion: This intervention, which comprises trained physiotherapists conducting conversations about increasing physical activity with their patients in a manner consistent with Motivational Interviewing as part of usual care combined with a specifically designed app, has potential to facilitate behaviour change with minimal extra therapist time

Particle Redistribution During Dendritic Solidification of Particle Suspensions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65783/1/j.1551-2916.2006.01094.x.pd

Coalminers' housing in Fife : company housing and social relations in Fife mining communities, 1870-1930

Fife coal-owners owned their workers houses and controlled the processes of housing provision and allocation. They were both employers and landlords. As a result the spheres of home and work were inextricably linked. This thesis examines the nature of the social relations arising from this "tied" relationship in the light of both local and national, political, economic and social developments, between 1870 and 1930. The themes of deference, paternalism, community, socialisation and social control, and the residual effects of pre-existing social relations, particularly pre-industrial relations of production, are explored. The empirical research concentrates upon the analysis of two coal companies in particular; the Fife Coal Company Ltd. and the Wemyss Coal Company. These companies operated coal mines in contrasting geographical locations; the former throughout inland west Fife and the latter along coastal south-east Fife. Each company built rows of colliers' houses in close proximity to the mines. At the beginning of the period housing for coal-miners was provided, not by speculative builders on the open market, but, by the coal-owners through their company architects and sub-contractors. Houses were provided as part of the employment contract as a means of attracting and maintaining the workforce. By the end of the period, the State, through the agency of local authorities, was the principal provider of working class housing in mining communities; coal companies had withdrawn from the housing market. The thesis attempts to explain this process in terms of changing social relations of production

Natural Language Understanding and Multimodal Discourse Analysis for Interpreting Extremist Communications and the Re-Use of These Materials Online

This paper reports on a study that is part of a project which aims to develop a multimodal analytical approach for big data analytics, initially in the context of violent extremism. The findings reported here tested the application of natural language processing models to the text of a sample of articles from the online magazines Dabiq and Rumiyah, produced by the Islamic extremist organisation ISIS. For comparison, text of articles found by reverse image search software which re-used the lead images from the original articles in text which either reported on or opposed extremist activities was also analysed. The aim was to explore what insights the natural language processing models could provide to distinguish between texts produced as propaganda to incite violent extremism and texts which either reported on or opposed violent extremism. The results showed that some valuable insights can be gained from such an approach and that these results could be improved through integrating automated analyses with a theoretical approach with analysed language and images in their immediate and social contexts. Such an approach will inform the interpretation of results and will be used in training software so that stronger results can be achieved in the future

Effect of heavy metals in recycled water used for household laundry on quality of cloth and washing machine

Recycled water for washing clothes saves significant amount of potable water and hence has a great potential for sustainable urban-water management. To date, there has been no official acceptance and very rare practice of use of recycled water for household laundry. This study investigates the effects of critical heavy metals (Pb, Mn, Fe, Cu and Zn) on cloth quality and corrosive/scaling of washing machine to evaluate the feasibility of using recycled water for household laundry. The experimental data can be used for future recycled-water-quality guidelines. Five representative cloth materials namely polyester, satin, polycotton, denim and organic cotton were selected for washing in tap water and synthetic recycled water which contained different concentrations of heavy metals. Cloth durability, surface morphology and textile colour of washed cloth samples were measured to investigate the effects of heavy metals on quality of fabric. Langelier Saturation Index (LSI) was used as the indicator for predicting corrosive/scaling effects on washing machine. The results indicated that quality of fabrics after 50 wash cycles was found to have no change by recycled water when concentration of Pb and Mn < 0.5 mg/L, Fe < 1 mg/L, Cu < 5 mg/L and Zn < 30 mg/L. Lower than the above values, the LSI indicated that recycled water would not lead to any negative impact on washing machine

FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model

Mathematical and computer models of epidemics have contributed to our understanding of the spread of infectious disease and the measures needed to contain or mitigate them. To help prepare for future influenza seasonal epidemics or pandemics, we developed a new stochastic model of the spread of influenza across a large population. Individuals in this model have realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza. The model has been calibrated so that outcomes are consistent with the 1957/1958 Asian A(H2N2) and 2009 pandemic A(H1N1) influenza viruses. We present examples of how this model can be used to study the dynamics of influenza epidemics in the United States and simulate how to mitigate or delay them using pharmaceutical interventions and social distancing measures. Computer simulation models play an essential role in informing public policy and evaluating pandemic preparedness plans. We have made the source code of this model publicly available to encourage its use and further development

Inconsistent strategies to spin up models in CMIP5: Implications for ocean biogeochemical model performance assessment

This is the final version of the article. Available from EGU via the DOI in this record.During the fifth phase of the Coupled Model Intercomparison Project (CMIP5) substantial efforts were made to systematically assess the skill of Earth system models. One goal was to check how realistically representative marine biogeochemical tracer distributions could be reproduced by models. In routine assessments model historical hindcasts were compared with available modern biogeochemical observations. However, these assessments considered neither how close modeled biogeochemical reservoirs were to equilibrium nor the sensitivity of model performance to initial conditions or to the spin-up protocols. Here, we explore how the large diversity in spin-up protocols used for marine biogeochemistry in CMIP5 Earth system models (ESMs) contributes to model-to-model differences in the simulated fields. We take advantage of a 500-year spin-up simulation of IPSL-CM5A-LR to quantify the influence of the spin-up protocol on model ability to reproduce relevant data fields. Amplification of biases in selected biogeochemical fields (O2, NO3, Alk-DIC) is assessed as a function of spin-up duration. We demonstrate that a relationship between spin-up duration and assessment metrics emerges from our model results and holds when confronted with a larger ensemble of CMIP5 models. This shows that drift has implications for performance assessment in addition to possibly aliasing estimates of climate change impact. Our study suggests that differences in spin-up protocols could explain a substantial part of model disparities, constituting a source of model-to-model uncertainty. This requires more attention in future model intercomparison exercises in order to provide quantitatively more correct ESM results on marine biogeochemistry and carbon cycle feedbacks.We sincerely thank I. Kriest, F. Joos, the anonymous reviewer and A. Yool for their useful comments on this paper. This work was supported by H2020 project CRESCENDO “Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach”, which received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 641816 and by the EU FP7 project CARBOCHANGE “Changes in carbon uptake and emissions by oceans in a changing climate” which received funding from the European community’s Seventh Framework Programme under grant agreement no. 264879. Supercomputing time was provided by GENCI (Grand Equipement National de Calcul Intensif) at CCRT (Centre de Calcul Recherche et Technologie), allocation 016178. Finally, we are grateful to the ESGF project which makes data available for all the community. Roland Séférian is grateful to Aurélien Ribes for his kind advices on statistics. Jerry Tjiputra acknowledges ORGANIC project (239965/F20) funded by the Research Council of Norway. Christoph Heinze and Jerry Tjiputra are grateful for support through project EVA – Earth system modelling of climate variations in the Anthropocene (229771/E10) funded by the Research Council of Norway, as well as CPU-time and mass storage provided through NOTUR project NN2345K as well as NorStore project NS2345K. Keith Lindsay and Scott C. Doney acknowledge support from the National Science Foundation